Microblogging During Small Scale Incidents

Date

August 27, 2013

Overview

Microblogs are increasingly gaining attention as an important information source in emergency management as a lot of valuable situational information is shared, both by citizens and official sources. However, current analyses focus on information shared during large scale incidents, with high amount of publicly available information. In contrast, in this talk we present the results of several studies on every day small scale incidents. The comparably low amount of information shared per event makes this significantly harder.

First, we show the results of a machine learning experiment for automatically detecting relevant information related to small scale incidents. With a precision of 82.2%, we are able to detect three different types of small scale incidents in microblogs. Second, we highlight the value of information present in microblogs for increasing situational awareness. For that, we demonstrate that incidents detected based on microblogs are correlated with real-world incidents. We also show the results of our analysis on user behavior during this type of incidents.

Speakers

Axel Schulz

Axel Schulz is a PhD student and works as a Research Associate at the Telecooperation Lab, Technische Universität Darmstadt and SAP Research, Darmstadt. He received his Diploma in Business and Computer Science from the same department in 2011. His PhD thesis is focused on making user-generated content usable for decision making in urban management. In this case, he focuses on the steps needed to fuse different types of user-generated content: geolocalization, classification, and filtering of microblogs and sensor data based on semantic web technologies and machine learning.

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